{"type":"ScholarlyArticle","title":"Learning Target-aware Representation for Visual Tracking via Informative Interactions","authors":[{"name":"Mingzhe Guo","slug":"mingzhe-guo","given":"Mingzhe","family":"Guo","affiliation":"Beijing Key Lab of Traffic Data Analysis and Mining , Beijing Jiaotong University"},{"name":"Zhipeng Zhang","slug":"zhipeng-zhang","given":"Zhipeng","family":"Zhang","affiliation":"National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences"},{"name":"Heng Fan","slug":"heng-fan","given":"Heng","family":"Fan","affiliation":"Department of Computer Science and Engineering , University of North Texas , Denton , TX USA"},{"name":"Liping Jing","slug":"liping-jing","given":"Liping","family":"Jing","affiliation":"Beijing Key Lab of Traffic Data Analysis and Mining , Beijing Jiaotong University"},{"name":"Yilin Lyu","slug":"yilin-lyu","given":"Yilin","family":"Lyu","affiliation":"Beijing Key Lab of Traffic Data Analysis and Mining , Beijing Jiaotong University"},{"name":"Bing Li","slug":"bing-li","given":"Bing","family":"Li","affiliation":"National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences"},{"name":"Weiming Hu","slug":"weiming-hu","given":"Weiming","family":"Hu","affiliation":"National Laboratory of Pattern Recognition , Institute of Automation , Chinese Academy of Sciences"}],"abstract":"We introduce a novel backbone architecture to improve target-perception ability of feature representation for tracking. Having observed de facto frameworks perform feature matching simply using the backbone outputs for target localization, there is no direct feedback from the matching module to the backbone network, especially the shallow layers. Concretely, only the matching module can directly access the target information, while the representation learning of candidate frame is blind to the reference target. Therefore, the accumulated target-irrelevant interference in shallow stages may degrade the feature quality of deeper layers. In this paper, we approach the problem by conducting multiple branch-wise interactions inside the Siamese-like backbone networks (InBN). The core of InBN is a general interaction modeler (GIM) that injects the target information to different stages of the backbone network, leading to better target-perception of candidate feature representation with negligible computation cost. The proposed GIM module and InBN mechanism are general and applicable to different backbone types including CNN and Transformer for improvements, as evidenced on multiple benchmarks. In particular, the CNN version improves the baseline with 3.2/6.9 absolute gains of SUC on LaSOT/TNL2K. The Transformer version obtains SUC of 65.7/52.0 on LaSOT/TNL2K, which are on par with recent SOTAs.","doi":"10.24963/ijcai.2022/130","url":"https://ijcai.org/proceedings/2022/130","pdf":"https://ijcai.org/proceedings/2022/0130.pdf","markdown":"https://ijcai.org/proceedings/2022/130.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/130","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Computer Vision","track":"Main Track","number":130,"pages":{"first":927,"last":934},"keywords":["Computer Vision: Motion and Tracking"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986961","length_sec":84,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985104","length_sec":557,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Bertinetto et al., 2016] Luca Bertinetto, Jack Valmadre, João F Henriques, Andrea Vedaldi, and Philip H S Torr. 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